Richard Socher

research

∙ 08/05/2021

The AI Economist: Optimal Economic Policy Design via Two-level Deep Reinforcement Learning

AI and reinforcement learning (RL) have improved many areas, but are not...

Stephan Zheng, et al. ∙

research

∙ 06/07/2021

Evaluating State-of-the-Art Classification Models Against Bayes Optimality

Evaluating the inherent difficulty of a given data-driven classification...

Ryan Theisen, et al. ∙

research

∙ 12/28/2020

Catastrophic Fisher Explosion: Early Phase Fisher Matrix Impacts Generalization

The early phase of training has been shown to be important in two ways f...

Stanisław Jastrzębski, et al. ∙

research

∙ 12/23/2020

Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing

We present BRIDGE, a powerful sequential architecture for modeling depen...

Xi Victoria Lin, et al. ∙

research

∙ 10/25/2020

Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference

Intent detection is one of the core components of goal-oriented dialog s...

Jian-Guo Zhang, et al. ∙

research

∙ 10/22/2020

Online Structured Meta-learning

Learning quickly is of great importance for machine intelligence deploye...

Huaxiu Yao, et al. ∙

research

∙ 10/18/2020

Explaining and Improving Model Behavior with k Nearest Neighbor Representations

Interpretability techniques in NLP have mainly focused on understanding ...

Nazneen Fatema Rajani, et al. ∙

research

∙ 10/14/2020

Explaining Creative Artifacts

Human creativity is often described as the mental process of combining a...

Lav R. Varshney, et al. ∙

research

∙ 10/06/2020

Universal Natural Language Processing with Limited Annotations: Try Few-shot Textual Entailment as a Start

A standard way to address different NLP problems is by first constructin...

Wenpeng Yin, et al. ∙

research

∙ 09/29/2020

GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing

We present GraPPa, an effective pre-training approach for table semantic...

Tao Yu, et al. ∙

research

∙ 09/21/2020

Composed Variational Natural Language Generation for Few-shot Intents

In this paper, we focus on generating training examples for few-shot int...

Congying Xia, et al. ∙

research

∙ 09/14/2020

GeDi: Generative Discriminator Guided Sequence Generation

Class-conditional language models (CC-LMs) can be used to generate natur...

Ben Krause, et al. ∙

research

∙ 09/09/2020

Central Yup'ik and Machine Translation of Low-Resource Polysynthetic Languages

Machine translation tools do not yet exist for the Yup'ik language, a po...

Christopher Liu, et al. ∙

research

∙ 07/30/2020

Photon: A Robust Cross-Domain Text-to-SQL System

Natural language interfaces to databases (NLIDB) democratize end user ac...

Jichuan Zeng, et al. ∙

research

∙ 07/24/2020

SummEval: Re-evaluating Summarization Evaluation

The scarcity of comprehensive up-to-date studies on evaluation metrics f...

Alexander R. Fabbri, et al. ∙

research

∙ 07/06/2020

DART: Open-Domain Structured Data Record to Text Generation

We introduce DART, a large dataset for open-domain structured data recor...

Dragomir Radev, et al. ∙

research

∙ 06/30/2020

Theory-Inspired Path-Regularized Differential Network Architecture Search

Despite its high search efficiency, differential architecture search (DA...

Pan Zhou, et al. ∙

research

∙ 06/26/2020

BERTology Meets Biology: Interpreting Attention in Protein Language Models

Transformer architectures have proven to learn useful representations fo...

Jesse Vig, et al. ∙

research

∙ 06/24/2020

Towards Understanding Hierarchical Learning: Benefits of Neural Representations

Deep neural networks can empirically perform efficient hierarchical lear...

Minshuo Chen, et al. ∙

research

∙ 06/24/2020

A High-Quality Multilingual Dataset for Structured Documentation Translation

This paper presents a high-quality multilingual dataset for the document...

Kazuma Hashimoto, et al. ∙

research

∙ 06/17/2020

CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization

The COVID-19 global pandemic has resulted in international efforts to un...

Andre Esteva, et al. ∙

research

∙ 06/05/2020

WOAD: Weakly Supervised Online Action Detection in Untrimmed Videos

Online action detection in untrimmed videos aims to identify an action a...

Mingfei Gao, et al. ∙

research

∙ 05/26/2020

EMT: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading

The goal of conversational machine reading is to answer user questions g...

Yifan Gao, et al. ∙

research

∙ 05/11/2020

Prototypical Contrastive Learning of Unsupervised Representations

This paper presents Prototypical Contrastive Learning (PCL), an unsuperv...

Junnan Li, et al. ∙

research

∙ 05/09/2020

It's Morphin' Time! Combating Linguistic Discrimination with Inflectional Perturbations

Training on only perfect Standard English corpora predisposes pre-traine...

Samson Tan, et al. ∙

research

∙ 05/02/2020

A Simple Language Model for Task-Oriented Dialogue

Task-oriented dialogue is often decomposed into three tasks: understandi...

Ehsan Hosseini-Asl, et al. ∙

research

∙ 05/02/2020

ESPRIT: Explaining Solutions to Physical Reasoning Tasks

Neural networks lack the ability to reason about qualitative physics and...

Nazneen Fatema Rajani, et al. ∙

research

∙ 04/28/2020

The AI Economist: Improving Equality and Productivity with AI-Driven Tax Policies

Tackling real-world socio-economic challenges requires designing and tes...

Stephan Zheng, et al. ∙

research

∙ 04/15/2020

ToD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogues

The use of pre-trained language models has emerged as a promising direct...

Chien-Sheng Wu, et al. ∙

research

∙ 03/30/2020

Improving out-of-distribution generalization via multi-task self-supervised pretraining

Self-supervised feature representations have been shown to be useful for...

Isabela Albuquerque, et al. ∙

research

∙ 03/03/2020

Towards Noise-resistant Object Detection with Noisy Annotations

Training deep object detectors requires significant amount of human-anno...

Junnan Li, et al. ∙

research

∙ 02/20/2020

Neural Bayes: A Generic Parameterization Method for Unsupervised Representation Learning

We introduce a parameterization method called Neural Bayes which allows ...

Devansh Arpit, et al. ∙

research

∙ 02/19/2020

Tree-structured Attention with Hierarchical Accumulation

Incorporating hierarchical structures like constituency trees has been s...

Xuan-Phi Nguyen, et al. ∙

research

∙ 02/19/2020

Non-Autoregressive Dialog State Tracking

Recent efforts in Dialogue State Tracking (DST) for task-oriented dialog...

Hung Le, et al. ∙

research

∙ 02/18/2020

DivideMix: Learning with Noisy Labels as Semi-supervised Learning

Deep neural networks are known to be annotation-hungry. Numerous efforts...

Junnan Li, et al. ∙

research

∙ 02/10/2020

Taylorized Training: Towards Better Approximation of Neural Network Training at Finite Width

We propose Taylorized training as an initiative towards better understan...

Yu Bai, et al. ∙

research

∙ 02/10/2020

Explore, Discover and Learn: Unsupervised Discovery of State-Covering Skills

Acquiring abilities in the absence of a task-oriented reward function is...

Victor Campos, et al. ∙

research

∙ 02/09/2020

Limits of Detecting Text Generated by Large-Scale Language Models

Some consider large-scale language models that can generate long and coh...

Lav R. Varshney, et al. ∙

research

∙ 12/11/2019

Learning from Noisy Anchors for One-stage Object Detection

State-of-the-art object detectors rely on regressing and classifying an ...

Hengduo Li, et al. ∙

research

∙ 11/24/2019

Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering

Answering questions that require multi-hop reasoning at web-scale necess...

Akari Asai, et al. ∙

research

∙ 11/09/2019

Attentive Student Meets Multi-Task Teacher: Improved Knowledge Distillation for Pretrained Models

In this paper, we explore the knowledge distillation approach under the ...

Linqing Liu, et al. ∙

research

∙ 11/08/2019

ERASER: A Benchmark to Evaluate Rationalized NLP Models

State-of-the-art models in NLP are now predominantly based on deep neura...

Jay DeYoung, et al. ∙

research

∙ 11/04/2019

Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards

While using shaped rewards can be beneficial when solving sparse reward ...

Alexander Trott, et al. ∙

research

∙ 10/28/2019

Sketch-Fill-A-R: A Persona-Grounded Chit-Chat Generation Framework

Human-like chit-chat conversation requires agents to generate responses ...

Michael Shum, et al. ∙

research

∙ 10/28/2019

Evaluating the Factual Consistency of Abstractive Text Summarization

Currently used metrics for assessing summarization algorithms do not acc...

Wojciech Kryściński, et al. ∙

research

∙ 10/22/2019

Global Capacity Measures for Deep ReLU Networks via Path Sampling

Classical results on the statistical complexity of linear models have co...

Ryan Theisen, et al. ∙

research

∙ 10/08/2019

Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking

Dialog State Tracking (DST) is a core component in task-oriented dialog ...

Jian-Guo Zhang, et al. ∙

research

∙ 10/01/2019

Entropy Penalty: Towards Generalization Beyond the IID Assumption

It has been shown that instead of learning actual object features, deep ...

Devansh Arpit, et al. ∙

research

∙ 09/11/2019

CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases

We present CoSQL, a corpus for building cross-domain, general-purpose da...

Tao Yu, et al. ∙

research

∙ 09/11/2019

CTRL: A Conditional Transformer Language Model for Controllable Generation

Large-scale language models show promising text generation capabilities,...

Nitish Shirish Keskar, et al. ∙

Richard Socher

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